The co-occurrences of two entities within sentences (a.k.a textual mentions) in a text corpus has shown its success for text-enhanced KGEs [1]}, [2]}. For example, the relational tuple in the Freebase KG (Joe Biden, president-of, US) is mentioned in the following sentence Joseph Robinette Biden Jr. is an American politician who is the 46th and current president of the United States.
This sentence expresses the president-of relation between the two entities Joe Biden and US.
As the entity-pair (Joe Biden,US) appears in a single sentence, we call it a with-mention entity-pair.
However, even in a large text corpus, not every related entity pair co-occurs in a specified window, which are referred to as without-mention entity-pairs in previous studies.
For instance, if we consider the widely used FB15K-237 KG [3]} and the ClueWeb12 [4]} text corpus with FB entity mention annotations,200 million sentences in CluWeb12 annotated with FB entity mention annotations. 33% of entity-pairs in FB15k-237 do not have textual mentions within the same sentences.
This sparseness problem limits the generalisation capabilities of using textual mentions for enhancing KGEs.
Specifically, [3]}, [1]} have shown larger improvements in link prediction for with-mention entity-pairs over without-mention pairs.
